yq VS jmespath.py

What I often use to just get the full key paths is yq (https://github.com/mikefarah/yq), piping into grep when necessary

  yq -o=props 

For more information about this command visit https://github.com/mikefarah/yq

I've been trying to install and config a plugin (papis.nvim) for a couple of days and am having issues with a function that uses io.popen to run yq to convert yaml files to json. I know my install of yq is fine- I can run yq -oj info.yaml from the command line with no issue and it produces the correct json output. I know the function can find the yq executable, but it returns nil. I've saved the error from the yq golang code: panic: runtime error: invalid memory address or nil pointer dereference

- yq has no if-then-else https://github.com/mikefarah/yq/issues/95 which is a poor design (or omission) in my opinion

After which, I use openapi-generator to make a yaml output.

https://gist.github.com/freshteapot/3637e8d2b5ecdf01b7d25246...

- yq version 3.4.1 (Worth noting, the example uses an out of date yq, so a few modifictaions might be needed)

https://github.com/mikefarah/yq

For those pining for a similar yaml query tool for working through acres of config: https://github.com/mikefarah/yq

jq is awesome and thanks to the new team for their recent efforts and energy, it massively appreciated.

For anyone looking for such a script, there's some CLIs that make it easy. One is `yq -o props` [1], another way is to use `yq -j` or `yj` [2] to convert to JSON and pipe it to `gron` [3].

[1] https://github.com/mikefarah/yq

[2] https://github.com/sclevine/yj

[3] https://github.com/tomnomnom/gron

The Azure CLI lets us write queries to filter the results of the az storage blob list command. The queries are written in JMESPath, which is a query language for JSON. In this case, we are filtering the results to only include blobs that end with the .bacpac extension and then selecting the first one as ordered by the lastModified property. If there are no blobs found, the script exits with a failure code. If we find a blob, we download it to the local path specified by the localPath variable.

For JSON there is the `jmespath` library which might help.

https://github.com/jmespath/jmespath.py

I love jq, but I also use JMESPath (especially with AWS CLI), yq (bundled with tomlq and xq as well), and dasel [2]. I also wish hclq [3] wasn't so dead!

[0]: https://jmespath.org/

Probably writing the query side of things is a lot of the fun here, but there is actually a spec (and a complying Rust impl) you can hook into for this JQ-like querying: https://jmespath.org/ ( https://github.com/jmespath/jmespath.rs ).

Thanks to custom variables that use labels, we can create various filters for the dashboard. You can look up my configuration of variables and extend it with an analogy way for your own needs. At the top, I marked the filter with detected pods in selected namespace. In the lower part, you can see a preview of all labels that are associated with a single log line. Most labels are meta information that Promtail adds during scraping targets. This part of the Promtail configuration provides it. In this section, I also marked a few labels that not comes out-of-the box e.g. leavel , class , thread . We added these labels using the Promtail json stage. You need to know that Promtail processes scraped logs in a pipeline. A pipeline is comprised of a set of stages. json stage is a parsing stage that reads the log line as JSON and accepts JMESPath expressions to extract data.

The tester on the official website for JMESPath (what json_query is doing) has been useful to me: https://jmespath.org/

Interesting. How does this compared to Jmespath? Not saying Jmespath is superior, just wondering whether you were aware of it.